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Learning interpretable networks from multivariate information in biological and clinical data
Multi angle
Authors : Isambert, Hervé (Author of the conference)
CIRM (Publisher )
68T05 - Learning and adaptive systems
92D10 - Genetics
CIRM (Publisher )
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Abstract :
The reconstruction of graphical models (or networks) has become ubiquitous to analyze the rapidly expanding, information-rich data of biological or clinical interest. I will outline some network reconstruction methods and applications to large scale datasets. In particular, our group has developped information-theoretic methods and machine learning tools to infer and analyze interpretable graphical models from large scale genomics data (single cell transcriptomics, tumor expression and mutation data) as well as clinical data (analysis of medical records from breast cancer patients, Institut Curie, and from elderly patients with cognitive disorders, La Pitie-Salpetriere).
Keywords : machine learning
MSC Codes : Keywords : machine learning
68T05 - Learning and adaptive systems
92D10 - Genetics
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Information on the Video
Film maker : Hennenfent, GuillaumeLanguage : English Available date : 23/03/2020 Conference Date : 03/03/2020 Subseries : Research talks arXiv category : Quantitative Biology ; Computer Science Mathematical Area(s) : Numerical Analysis & Scientific Computing ; Computer Science ; Probability & Statistics Format : MP4 (.mp4) - HD Video Time : 01:38:23 Targeted Audience : Researchers Download : https://videos.cirm-math.fr/2020-03-03_Isambert.mp4 
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Information on the Event
Event Title : Thematic Month Week 5: Networks and Molecular Biology / Mois thématique Semaine 5 : Réseaux et biologie moléculaireEvent Organizers : Baudot, Anais ; Hubert, Florence ; Mossé, Brigitte ; Rémy, Elisabeth ; Tichit, Laurent ; Vignes, Matthieu Dates : 02/03/2020 - 06/03/2020 Event Year : 2020 Event URL : https://conferences.cirm-math.fr/2305.html
Citation Data
DOI : 10.24350/CIRM.V.19619803Cite this video as: Isambert, Hervé (2020). Learning interpretable networks from multivariate information in biological and clinical data. CIRM. Audiovisual resource. doi:10.24350/CIRM.V.19619803 URI : http://dx.doi.org/10.24350/CIRM.V.19619803 |
See Also
- [Multi angle] Part 2 - Dynamic logic models complement machine learning for personalized medicine / Author of the conference Saez-Rodriguez, Julio.
- [Multi angle] Matrix factorisation techniques for data integration / Author of the conference Lê Cao, Kim-Anh.
- [Post-edited] Network-guided feature selection in high-dimensional genomic data / Author of the conference Azencott, Chloé.
- [Multi angle] Cell processes in space and time - exemplified with yeast mating / Author of the conference Klipp, Edda.
- [Multi angle] Network approaches for personalized medicine / Author of the conference Martinez-Rodriguez, Maria.
- [Multi angle] From data to biological processes: interactions, networks and analyses, to understand cellular functions / Author of the conference Brun, Christine.
- [Multi angle] Part 1 - Networks of prior knowledge as frames to understand complex biological data / Author of the conference Saez-Rodriguez, Julio.
Bibliography
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